Dynamic feedback for determining collection-set size

ABSTRACT

A garbage collector collects a generation of a dynamically allocated heap in a computer&#39;s memory incrementally. A collection set within the generation is associated with each collection increment. The collector reclaims for reuse the memory space occupied by any collection-set object not reachable by a reference chain that extends from outside the collection set. The collector monitors the total amount of allocation that occurs within the generation between collection increments, and it bases the collection-set size on those allocation amounts.

BACKGROUND OF THE INVENTION

[0001] 1. Field of the Invention

[0002] The present invention is directed to garbage collection ofdynamically allocated memory. It particularly concerns the rate at whichgarbage collection should occur.

[0003] 2. Background Information

[0004] In the field of computer systems, considerable effort has beenexpended on the task of allocating memory to data objects. For thepurposes of this discussion, the term object refers to a data structurerepresented in a computer system's memory. Other terms sometimes usedfor the same concept are record and structure. An object may beidentified by a reference, a relatively small amount of information thatcan be used to access the object. A reference can be represented as a“pointer” or a “machine address,” which may require, for instance, onlysixteen, thirty-two, or sixty-four bits of information, although thereare other ways to represent a reference.

[0005] In some systems, which are usually known as “object oriented,”objects may have associated methods, which are routines that can beinvoked by reference to the object. They also may belong to a class,which is an organizational entity that may contain method code or otherinformation shared by all objects belonging to that class. In thediscussion that follows, though, the term object will not be limited tosuch structures; it will additionally include structures with whichmethods and classes are not associated.

[0006] The invention to be described below is applicable to systems thatallocate memory to objects dynamically. Not all systems employ dynamicallocation. In some computer languages, source programs must be sowritten that all objects to which the program's variables refer arebound to storage locations at compile time. This storage-allocationapproach, sometimes referred to as “static allocation,” is the policytraditionally used by the Fortran programming language, for example.

[0007] Even for compilers that are thought of as allocating objects onlystatically, of course, there is often a certain level of abstraction tothis binding of objects to storage locations. Consider the typicalcomputer system 10 depicted in FIG. 1, for example. Data, andinstructions for operating on them, that a microprocessor 11 uses mayreside in on-board cache memory or be received from further cache memory12, possibly through the mediation of a cache controller 13. Thatcontroller 13 can in turn receive such data from system read/writememory (“RAM”) 14 through a RAM controller 15 or from various peripheraldevices through a system bus 16. The memory space made available to anapplication program may be “virtual” in the sense that it may actuallybe considerably larger than RAM 14 provides. So the RAM contents will beswapped to and from a system disk 17.

[0008] Additionally, the actual physical operations performed to accesssome of the most-recently visited parts of the process's address spaceoften will actually be performed in the cache 12 or in a cache on boardmicroprocessor 11 rather than on the RAM 14, with which those cachesswap data and instructions just as RAM 14 and system disk 17 do witheach other.

[0009] A further level of abstraction results from the fact that anapplication will often be run as one of many processes operatingconcurrently with the support of an underlying operating system. As partof that system's memory management, the application's memory space maybe moved among different actual physical locations many times in orderto allow different processes to employ shared physical memory devices.That is, the location specified in the application's machine code mayactually result in different physical locations at different timesbecause the operating system adds different offsets to themachine-language-specified location.

[0010] Despite these expedients, the use of static memory allocation inwriting certain long-lived applications makes it difficult to restrictstorage requirements to the available memory space. Abiding by spacelimitations is easier when the platform provides for dynamic memoryallocation, i.e., when memory space to be allocated to a given object isdetermined only at run time.

[0011] Dynamic allocation has a number of advantages, among which isthat the run-time system is able to adapt allocation to run-timeconditions. For example, the programmer can specify that space should beallocated for a given object only in response to a particular run-timecondition. The C-language library function malloc( ) is often used forthis purpose. Conversely, the programmer can specify conditions underwhich memory previously allocated to a given object can be reclaimed forreuse. The C-language library function free() results in such memoryreclamation.

[0012] Because dynamic allocation provides for memory reuse, itfacilitates generation of large or long-lived applications, which overthe course of their lifetimes may employ objects whose total memoryrequirements would greatly exceed the available memory resources if theywere bound to memory locations statically.

[0013] Particularly for long-lived applications, though, allocation andreclamation of dynamic memory must be performed carefully. If theapplication fails to reclaim unused memory—or, worse, loses track of theaddress of a dynamically allocated segment of memory—its memoryrequirements will grow over time to exceed the system's availablememory. This kind of error is known as a “memory leak.”

[0014] Another kind of error occurs when an application reclaims memoryfor reuse even though it still maintains a reference to that memory. Ifthe reclaimed memory is reallocated for a different purpose, theapplication may inadvertently manipulate the same memory in multipleinconsistent ways. This kind of error is known as a “danglingreference,” because an application should not retain a reference to amemory location once that location is reclaimed. Explicit dynamic-memorymanagement by using interfaces like malloc( )/free( ) often leads tothese problems.

[0015] A way of reducing the likelihood of such leaks and related errorsis to provide memory-space reclamation in a more-automatic manner.Techniques used by systems that reclaim memory space automatically arecommonly referred to as “garbage collection.” Garbage collectors operateby reclaiming space that they no longer consider “reachable.” Staticallyallocated objects represented by a program's global variables arenormally considered reachable throughout a program's life. Such objectsare not ordinarily stored in the garbage collector's managed memoryspace, but they may contain references to dynamically allocated objectsthat are, and such objects are considered reachable. Clearly, an objectreferred to in the processor's call stack is reachable, as is an objectreferred to by register contents. And an object referred to by anyreachable object is also reachable.

[0016] The use of garbage collectors is advantageous because, whereas aprogrammer working on a particular sequence of code can perform his taskcreditably in most respects with only local knowledge of the applicationat any given time, memory allocation and reclamation require a globalknowledge of the program. Specifically, a programmer dealing with agiven sequence of code does tend to know whether some portion of memoryis still in use for that sequence of code, but it is considerably moredifficult for him to know what the rest of the application is doing withthat memory. By tracing references from some conservative notion of a“root set,” e.g., global variables, registers, and the call stack,automatic garbage collectors obtain global knowledge in a methodicalway. By using a garbage collector, the programmer is relieved of theneed to worry about the application's global state and can concentrateon local-state issues, which are more manageable. The result isapplications that are more robust, having no dangling references andfewer memory leaks.

[0017] Garbage-collection mechanisms can be implemented by various partsand levels of a computing system. One approach is simply to provide themas part of a batch compiler's output. Consider FIG. 2's simplebatch-compiler operation, for example. A computer system executes inaccordance with compiler object code and therefore acts as a compiler20. The compiler object code is typically stored on a medium such asFIG. 1's system disk 17 or some other machine-readable medium, and it isloaded into RAM 14 to configure the computer system to act as acompiler. In some cases, though, the compiler object code's persistentstorage may instead be provided in a server system remote from themachine that performs the compiling. The electrical signals that carrythe digital data by which the computer systems exchange that code areexamples of the kinds of electromagnetic signals by which the computerinstructions can be communicated. Others are radio waves, microwaves,and both visible and invisible light.

[0018] The input to the compiler is the application source code, and theend product of the compiler process is application object code. Thisobject code defines an application 21, which typically operates on inputsuch as mouse clicks, etc., to generate a display or some other type ofoutput. This object code implements the relationship that the programmerintends to specify by his application source code. In one approach togarbage collection, the compiler 20, without the programmer's explicitdirection, additionally generates code that automatically reclaimsunreachable memory space.

[0019] Even in this simple case, though, there is a sense in which theapplication does not itself provide the entire garbage collector.Specifically, the application will typically call upon the underlyingoperating system's memory-allocation functions. And the operating systemmay in turn take advantage of various hardware that lends itselfparticularly to use in garbage collection. So even a very simple systemmay disperse the garbage-collection mechanism over a number ofcomputer-system layers.

[0020] To get some sense of the variety of system components that can beused to implement garbage collection, consider FIG. 3's example of amore complex way in which various levels of source code can result inthe machine instructions that a processor executes. In the FIG. 3arrangement, the human applications programmer produces source code 22written in a high-level language. A compiler 23 typically converts thatcode into “class files.” These files include routines written ininstructions, called “byte codes” 24, for a “virtual machine” thatvarious processors can be software-configured to emulate. Thisconversion into byte codes is almost always separated in time from thosecodes' execution, so FIG. 3 divides the sequence into a “compile-timeenvironment” 25 separate from a “run-time environment” 26, in whichexecution occurs. One example of a high-level language for whichcompilers are available to produce such virtual-machine instructions isthe Java™ programming language. (Java is a trademark or registeredtrademark of Sun Microsystems, Inc., in the United States and othercountries.)

[0021] Most typically, the class files' byte-code routines are executedby a processor under control of a virtual-machine process 27. Thatprocess emulates a virtual machine from whose instruction set the bytecodes are drawn. As is true of the compiler 23, the virtual-machineprocess 27 may be specified by code stored on a local disk or some othermachine-readable medium from which it is read into FIG. 1's RAM 14 toconfigure the computer system to implement the garbage collector andotherwise act as a virtual machine. Again, though, that code'spersistent storage may instead be provided by a server system remotefrom the processor that implements the virtual machine, in which casethe code would be transmitted electrically or optically to thevirtual-machine-implementing processor.

[0022] In some implementations, much of the virtual machine's action inexecuting these byte codes is most like what those skilled in the artrefer to as “interpreting,” so FIG. 3 depicts the virtual machine asincluding an “interpreter” 28 for that purpose. In addition to orinstead of running an interpreter, many virtual-machine implementationsactually compile the byte codes concurrently with the resultant objectcode's execution, so FIG. 3 depicts the virtual machine as additionallyincluding a “just-in-time” compiler 29. We will refer to thejust-in-time compiler and the interpreter together as “executionengines” since they are the methods by which byte code can be executed.

[0023] Now, some of the functionality that source-language constructsspecify can be quite complicated, requiring many machine-languageinstructions for their implementation. One quite-common example is asource-language instruction that calls for 64-bit arithmetic on a 32-bitmachine. More germane to the present invention is the operation ofdynamically allocating space to a new object; the allocation of suchobjects must be mediated by the garbage collector.

[0024] In such situations, the compiler may produce “inline” code toaccomplish these operations. That is, all object-code instructions forcarrying out a given source-code-prescribed operation will be repeatedeach time the source code calls for the operation. But inlining runs therisk that “code bloat” will result if the operation is invoked at manysource-code locations.

[0025] The natural way of avoiding this result is instead to provide theoperation's implementation as a procedure, i.e., a single code sequencethat can be called from any location in the program. In the case ofcompilers, a collection of procedures for implementing many types ofsource-code-specified operations is called a runtime system for thelanguage. The execution engines and the runtime system of a virtualmachine are designed together so that the engines “know” whatruntime-system procedures are available in the virtual machine (and onthe target system if that system provides facilities that are directlyusable by an executing virtual-machine program.) So, for example, thejust-in-time compiler 29 may generate native code that includes calls tomemory-allocation procedures provided by the virtual machine's runtimesystem. These allocation routines may in turn invoke garbage-collectionroutines of the runtime system when there is not enough memory availableto satisfy an allocation. To represent this fact, FIG. 3 includes block30 to show that the compiler's output makes calls to the runtime systemas well as to the operating system 31, which consists of procedures thatare similarly system-resident but are not compiler-dependent.

[0026] Although the FIG. 3 arrangement is a popular one, it is by nomeans universal, and many further implementation types can be expected.Proposals have even been made to implement the virtual machine 27'sbehavior in a hardware processor, in which case the hardware itselfwould provide some or all of the garbage-collection function.

[0027] The arrangement of FIG. 3 differs from FIG. 2 in that thecompiler 23 for converting the human programmer's code does notcontribute to providing the garbage-collection function; that resultslargely from the virtual machine 27's operation. Those skilled in thatart will recognize that both of these organizations are merelyexemplary, and many modern systems employ hybrid mechanisms, whichpartake of the characteristics of traditional compilers and traditionalinterpreters both.

[0028] The invention to be described below is applicable independentlyof whether a batch compiler, a just-in-time compiler, an interpreter, orsome hybrid is employed to process source code. In the remainder of thisapplication, therefore, we will use the term compiler to refer to anysuch mechanism, even if it is what would more typically be called aninterpreter.

[0029] In short, garbage collectors can be implemented in a wide rangeof combinations of hardware and/or software. As is true of most of thegarbage-collection techniques described in the literature, the inventionto be described below is applicable to most such systems.

[0030] By implementing garbage collection, a computer system can greatlyreduce the occurrence of memory leaks and other software deficiencies inwhich human programming frequently results. But it can also havesignificant adverse performance effects if it is not implementedcarefully. To distinguish the part of the program that does “useful”work from that which does the garbage collection, the term mutator issometimes used in discussions of these effects; from the collector'spoint of view, what the mutator does is mutate active data structures'connectivity.

[0031] Some garbage-collection approaches rely heavily on interleavinggarbage-collection steps among mutator steps. In one type ofgarbage-collection approach, for instance, the mutator operation ofwriting a reference is followed immediately by garbage-collector stepsused to maintain a reference count in that object's header, and code forsubsequent new-object storage includes steps for finding space occupiedby objects whose reference count has fallen to zero. Obviously, such anapproach can slow mutator operation significantly.

[0032] Other approaches therefore interleave very fewgarbage-collector-related instructions into the main mutator process butinstead interrupt it from time to time to perform garbage-collectioncycles, in which the garbage collector finds unreachable objects andreclaims their memory space for reuse. Such an approach will be assumedin discussing FIG. 4's depiction of a simple garbage-collectionoperation. Within the memory space allocated to a given application is apart 40 managed by automatic garbage collection. In the followingdiscussion, this will be referred to as the “heap,” although in othercontexts that term refers to all dynamically allocated memory. Duringthe course of the application's execution, space is allocated forvarious objects 42, 44, 46, 48, and 50. Typically, the mutator allocatesspace within the heap by invoking the garbage collector, which at somelevel manages access to the heap. Basically, the mutator asks thegarbage collector for a pointer to a heap region where it can safelyplace the object's data. The garbage collector keeps track of the factthat the thus-allocated region is occupied. It will refrain fromallocating that region in response to any other request until itdetermines that the mutator no longer needs the region allocated to thatobject.

[0033] Garbage collectors vary as to which objects they considerreachable and unreachable. For the present discussion, though, an objectwill be considered “reachable” if it is referred to, as object 42 is, bya reference in the root set 52. The root set consists of referencevalues stored in the mutator's threads' call stacks, the CPU registers,and global variables outside the garbage-collected heap. An object isalso reachable if it is referred to, as object 46 is, by anotherreachable object (in this case, object 42). Objects that are notreachable can no longer affect the program, so it is safe to re-allocatethe memory spaces that they occupy.

[0034] A typical approach to garbage collection is therefore to identifyall reachable objects and reclaim any previously allocated memory thatthe reachable objects do not occupy. A typical garbage collector mayidentify reachable objects by tracing references from the root set 52.For the sake of simplicity, FIG. 4 depicts only one reference from theroot set 52 into the heap 40. (Those skilled in the art will recognizethat there are many ways to identify references, or at least datacontents that may be references.) The collector notes that the root setpoints to object 42, which is therefore reachable, and that reachableobject 42 points to object 46, which therefore is also reachable. Butthose reachable objects point to no other objects, so objects 44, 48,and 50 are all unreachable, and their memory space may be reclaimed.This may involve, say, placing that memory space in a list of freememory blocks.

[0035] To avoid excessive heap fragmentation, some garbage collectorsadditionally relocate reachable objects. FIG. 5 shows a typicalapproach. The heap is partitioned into two halves, hereafter called“semi-spaces.” For one garbage-collection cycle, all objects areallocated in one semi-space 54, leaving the other semi-space 56 free.When the garbage-collection cycle occurs, objects identified asreachable are “evacuated” to the other semi-space 56, so all ofsemi-space 54 is then considered free. Once the garbage-collection cyclehas occurred, all new objects are allocated in the lower semi-space 56until yet another garbage-collection cycle occurs, at which time thereachable objects are evacuated back to the upper semi-space 54.

[0036] Although this relocation requires the extra steps of copying thereachable objects and updating references to them, it tends to be quiteefficient, since most new objects quickly become unreachable, so most ofthe current semi-space is actually garbage. That is, only a relativelyfew, reachable objects need to be relocated, after which the entiresemi-space contains only garbage and can be pronounced free forreallocation.

[0037] Now, a collection cycle can involve following all referencechains from the basic root set—i.e., from inherently reachable locationssuch as the call stacks, class statics and other global variables, andregisters—and reclaiming all space occupied by objects not encounteredin the process. And the simplest way of performing such a cycle is tointerrupt the mutator to provide a collector interval in which theentire cycle is performed before the mutator resumes. For certain typesof applications, this approach to collection-cycle scheduling isacceptable and, in fact, highly efficient.

[0038] For many interactive and real-time applications, though, thisapproach is not acceptable. The delay in mutator operation that thecollection cycle's execution causes can be annoying to a user and canprevent a real-time application from responding to its environment withthe required speed. In some applications, choosing collection timesopportunistically can reduce this effect. Collection intervals can beinserted when an interactive mutator reaches a point at which it awaitsuser input, for instance.

[0039] So it may often be true that the garbage-collection operation'seffect on performance can depend less on the total collection time thanon when collections actually occur. But another factor that often iseven more determinative is the duration of any single collectioninterval, i.e., how long the mutator must remain quiescent at any onetime. In an interactive system, for instance, a user may never noticehundred-millisecond interruptions for garbage collection, whereas mostusers would find interruptions lasting for two seconds to be annoying.

[0040] The cycle may therefore be divided up among a plurality ofcollector intervals. When a collection cycle is divided up among aplurality of collection intervals, it is only after a number ofintervals that the collector will have followed all reference chains andbe able to identify as garbage any objects not thereby reached. Thisapproach is more complex than completing the cycle in a singlecollection interval; the mutator will usually modify references betweencollection intervals, so the collector must repeatedly update its viewof the reference graph in the midst of the collection cycle. To makesuch updates practical, the mutator must communicate with the collectorto let it know what reference changes are made between intervals.

[0041] An even more complex approach, which some systems use toeliminate discrete pauses or maximize resource-use efficiency, is toexecute the mutator and collector in concurrent execution threads. Mostsystems that use this approach use it for most but not all of thecollection cycle; the mutator is usually interrupted for a shortcollector interval, in which a part of the collector cycle takes placewithout mutation.

[0042] Independent of whether the collection cycle is performedconcurrently with mutator operation, is completed in a single interval,or extends over multiple intervals is the question of whether the cycleis complete, as has tacitly been assumed so far, or is instead“incremental.” In incremental collection, a collection cycle constitutesonly an increment of collection: the collector does not follow allreference chains from the basic root set completely. Instead, itconcentrates on only a portion, or collection set, of the heap.Specifically, it identifies every collection-set object referred to by areference chain that extends into the collection set from outside of it,and it reclaims the collection-set space not occupied by such objects,possibly after evacuating them from the collection set.

[0043] By thus culling objects referenced by reference chains that donot necessarily originate in the basic root set, the collector can bethought of as expanding the root set to include as roots some locationsthat may not be reachable. Although incremental collection therebyleaves “floating garbage,” it can result in relatively low pause timeseven if entire collection increments are completed during respectivesingle collection intervals.

[0044] Most collectors that employ incremental collection operate in“generations” although this is not necessary in principle. Differentportions, or generations, of the heap are subject to differentcollection policies. New objects are allocated in a “young” generation,and older objects are promoted from younger generations to older or more“mature” generations. Collecting the younger generations more frequentlythan the others yields greater efficiency because the youngergenerations tend to accumulate garbage faster; newly allocated objectstend to “die,” while older objects tend to “survive.”

[0045] But generational collection greatly increases what is effectivelythe root set for a given generation. Consider FIG. 6, which depicts aheap as organized into three generations 58, 60, and 62. Assume thatgeneration 60 is to be collected. The process for this individualgeneration may be more or less the same as that described in connectionwith FIGS. 4 and 5 for the entire heap, with one major exception. In thecase of a single generation, the root set must be considered to includenot only the call stack, registers, and global variables represented byset 52 but also objects in the other generations 58 and 62, whichthemselves may contain references to objects in generation 60. Sopointers must be traced not only from the basic root set 52 but alsofrom objects within the other generations.

[0046] One could perform this tracing by simply inspecting allreferences in all other generations at the beginning of every collectioninterval, and it turns out that this approach is actually feasible insome situations. But it takes too long in other situations, so workersin this field have employed a number of approaches to expeditingreference tracing. One approach is to include so-called write barriersin the mutator process. A write barrier is code added to a writeoperation to record information from which the collector can determinewhere references were written or may have been since the last collectioninterval. A reference list can then be maintained by taking such a listas it existed at the end of the previous collection interval andupdating it by inspecting only locations identified by the write barrieras possibly modified since the last collection interval.

[0047] One of the many write-barrier implementations commonly used byworkers in this art employs what has been referred to as the “cardtable.” FIG. 6 depicts the various generations as being divided intosmaller sections, known for this purpose as “cards.” Card tables 64, 66,and 68 associated with respective generations contain an entry for eachof their cards. When the mutator writes a reference in a card, it makesan appropriate entry in the card-table location associated with thatcard (or, say, with the card in which the object containing thereference begins). Most write-barrier implementations simply make aBoolean entry indicating that the write operation has been performed,although some may be more elaborate. The mutator having thus left arecord of where new or modified references may be, the collector canthereafter prepare appropriate summaries of that information, as will beexplained in due course. For the sake of concreteness, we will assumethat the summaries are maintained by steps that occur principally at thebeginning of each collection interval.

[0048] Of course, there are other write-barrier approaches, such assimply having the write barrier add to a list of addresses wherereferences where written. Also, although there is no reason in principleto favor any particular number of generations, and although FIG. 6 showsthree, most generational garbage collectors have only two generations,of which one is the young generation and the other is the maturegeneration. Moreover, although FIG. 6 shows the generations as being ofthe same size, a more-typical configuration is for the young generationto be considerably smaller. Finally, although we assumed for the sake ofsimplicity that collection during a given interval was limited to onlyone generation, a more-typical approach is actually to collect the wholeyoung generation at every interval but to collect the mature one lessfrequently.

[0049] Some collectors collect the entire young generation in everyinterval and may thereafter perform mature-generation collection in thesame interval. It may therefore take relatively little time to scan allyoung-generation objects remaining after young-generation collection tofind references into the mature generation. Even when such collectors douse card tables, therefore, they often do not use them for findingyoung-generation references that refer to mature-generation objects. Onthe other hand, laboriously scanning the entire mature generation forreferences to young-generation (or mature-generation) objects wouldordinarily take too long, so the collector uses the card table to limitthe amount of memory it searches for mature-generation references.

[0050] Now, although it typically takes very little time to collect theyoung generation, it may take more time than is acceptable within asingle garbage-collection interval to collect the entire maturegeneration. So some garbage collectors may collect the mature generationincrementally; that is, they may perform only a part of the maturegeneration's collection during any particular collection cycle.Incremental collection presents the problem that, since the generation'sunreachable objects outside the “collection set” of objects processedduring that cycle cannot be recognized as unreachable, collection-setobjects to which they refer tend not to be, either.

[0051] To reduce the adverse effect this would otherwise have oncollection efficiency, workers in this field have employed the “trainalgorithm,” which FIG. 7 depicts. A generation to be collectedincrementally is divided into sections, which for reasons about to bedescribed are referred to as “car sections.” Conventionally, ageneration's incremental collection occurs in fixed-size sections, and acar section's size is that of the generation portion to be collectedduring one cycle.

[0052] The discussion that follows will occasionally employ thenomenclature in the literature by using the term car instead of carsection. But the literature seems to use that term to refer variouslynot only to memory sections themselves but also to data structures thatthe train algorithm employs to manage them when they contain objects, aswell as to the more-abstract concept that the car section and managingdata structure represent in discussions of the algorithm. So thefollowing discussion will more frequently use the expression car sectionto emphasize the actual sections of memory space for whose managementthe car concept is employed.

[0053] According to the train algorithm, the car sections are groupedinto “trains,” which are ordered, conventionally according to age. Forexample, FIG. 7 shows an oldest train 73 consisting of a generation 74'sthree car sections described by associated data structures 75, 76, and78, while a second train 80 consists only of a single car section,represented by structure 82, and the youngest train 84 (referred to asthe “allocation train”) consists of car sections that data structures 86and 88 represent. As will be seen below, car sections' train membershipscan change, and any car section added to a train is typically added tothe end of a train.

[0054] Conventionally, the car collected in an increment is the oneadded earliest to the oldest train, which in this case is car 75. All ofthe generation's cars can thus be thought of as waiting for collectionin a single long line, in which cars are ordered in accordance with theorder of the trains to which they belong and, within trains, inaccordance with the order in which they were added to those trains.

[0055] As is usual, the way in which reachable objects are identified isto determine whether there are references to them in the root set or inany other object already determined to be reachable. In accordance withthe train algorithm, the collector additionally performs a test todetermine whether there are any references at all from outside theoldest train to objects within it. If there are not, then all carswithin the train can be reclaimed, even though not all of those cars arein the collection set. And the train algorithm so operates thatinter-car references tend to be grouped into trains, as will now beexplained.

[0056] To identify references into the car from outside of it,train-algorithm implementations typically employ “remembered sets.” Ascard tables are, remembered sets are used to keep track of references.Whereas a card-table entry contains information about references thatthe associated card contains, though, a remembered set associated with agiven region contains information about references into that region fromlocations outside of it. In the case of the train algorithm, rememberedsets are associated with car sections. Each remembered set, such as car75's remembered set 90, lists locations in the generation that containreferences into the associated car section.

[0057] The remembered sets for all of a generation's cars are typicallyupdated at the start of each collection interval. To illustrate how suchupdating and other collection operations may be carried out, FIG. 8depicts an operational sequence in a system of the typical typementioned above. That is, it shows a sequence of operations that mayoccur in a system in which the entire garbage-collected heap is dividedinto two generations, namely, a young generation and an old generation,and in which the young generation is much smaller than the oldgeneration. FIG. 8 is also based on the assumption and that the trainalgorithm is used only for collecting the old generation.

[0058] Block 102 represents a period of the mutator's operation. As wasexplained above, the mutator makes a card-table entry to identify anycard that it has “dirtied” by adding or modifying a reference that thecard contains. At some point, the mutator will be interrupted forcollector operation. Different implementations employ different eventsto trigger such an interruption, but we will assume for the sake ofconcreteness that the system's dynamic-allocation routine causes suchinterruptions when no room is left in the young generation for anyfurther allocation. A dashed line 103 represents the transition frommutator operation and collector operation.

[0059] In the system assumed for the FIG. 8 example, the collectorcollects the (entire) young generation each time such an interruptionoccurs. When the young generation's collection ends, the mutatoroperation usually resumes, without the collector's having collected anypart of the old generation. Once in a while, though, the collector alsocollects part of the old generation, and FIG. 8 is intended toillustrate such an occasion.

[0060] When the collector's interval first starts, it first processesthe card table, in an operation that block 104 represents. As wasmentioned above, the collector scans the “dirtied” cards for referencesinto the young generation. If a reference is found, that fact ismemorialized appropriately. If the reference refers to ayoung-generation object, for example, an expanded card table may be usedfor this purpose. For each card, such an expanded card table mightinclude a multi-byte array used to summarize the card's referencecontents. The summary may, for instance, be a list of offsets thatindicate the exact locations within the card of references toyoung-generation objects, or it may be a list of fine-granularity“sub-cards” within which references to young-generation objects may befound. If the reference refers to an old-generation object, thecollector often adds an entry to the remembered set associated with thecar containing that old-generation object. The entry identifies thereference's location, or at least a small region in which the referencecan be found. For reasons that will become apparent, though, thecollector will typically not bother to place in the remembered set thelocations of references from objects in car sections farther forward inthe collection queue than the referred-to object, i.e., from objects inolder trains or in cars added earlier to the same train.

[0061] The collector then collects the young generation, as block 105indicates. (Actually, young-generation collection may be interleavedwith the dirty-region scanning, but the drawing illustrates it forpurpose of explanation as being separate.) If a young-generation objectis referred to by a reference that card-table scanning has revealed,that object is considered to be potentially reachable, as is anyyoung-generation object referred to by a reference in the root set or inanother reachable young-generation object. The space occupied by anyyoung-generation object thus considered reachable is withheld fromreclamation. For example, it may be evacuated to a young-generationsemi-space that will be used for allocation during the next mutatorinterval. It may instead be promoted into the older generation, where itis placed into a car containing a reference to it or into a car in thelast train. Or some other technique may be used to keep the memory spaceit occupies off the system's free list. The collector then reclaims anyyoung-generation space occupied by any other objects, i.e., by anyyoung-generation objects not identified as transitively reachablethrough references located outside the young generation.

[0062] The collector then performs the train algorithm's central test,referred to above, of determining whether there are any references intothe oldest train from outside of it. As was mentioned above, the actualprocess of determining, for each object, whether it can be identified asunreachable is performed for only a single car section in any cycle. Inthe absence of features such as those provided by the train algorithm,this would present a problem, because garbage structures may be largerthan a car section. Objects in such structures would therefore(erroneously) appear reachable, since they are referred to from outsidethe car section under consideration. But the train algorithmadditionally keeps track of whether there are any references into agiven car from outside the train to which it belongs, and trains' sizesare not limited. As will be apparent presently, objects not found to beunreachable are relocated in such a way that garbage structures tend tobe gathered into respective trains into which, eventually, no referencesfrom outside the train point. If no references from outside the trainpoint to any objects inside the train, the train can be recognized ascontaining only garbage. This is the test that block 106 represents. Allcars in a train thus identified as containing only garbage can bereclaimed.

[0063] The question of whether old-generation references point into thetrain from outside of it is (conservatively) answered in the course ofupdating remembered sets; in the course of updating a car's rememberedset, it is a simple matter to flag the car as being referred to fromoutside the train. The step-106 test additionally involves determiningwhether any references from outside the old generation point into theoldest train. Various approaches to making this determination have beensuggested, including the conceptually simple approach of merelyfollowing all reference chains from the root set until those chains (1)terminate, (2) reach an old-generation object outside the oldest train,or (3) reach an object in the oldest train. In the two-generationexample, most of this work can be done readily by identifying referencesinto the collection set from live young-generation objects during theyoung-generation collection. If one or more such chains reach the oldesttrain, that train includes reachable objects. It may also includereachable objects if the remembered-set-update operation has found oneor more references into the oldest train from outside of it. Otherwise,that train contains only garbage, and the collector reclaims all of itscar sections for reuse, as block 107 indicates. The collector may thenreturn control to the mutator, which resumes execution, as FIG. 8B'sblock 108 indicates.

[0064] If the train contains reachable objects, on the other hand, thecollector turns to evacuating potentially reachable objects from thecollection set. The first operation, which block 110 represents, is toremove from the collection set any object that is reachable from theroot set by way of a reference chain that does not pass through the partof the old generation that is outside of the collection set. In theillustrated arrangement, in which there are only two generations, andthe young generation has previously been completely collected during thesame interval, this means evacuating from a collection set any objectthat (1) is directly referred to by a reference in the root set, (2) isdirectly referred to by a reference in the young generation (in which noremaining objects have been found unreachable), or (3) is referred to byany reference in an object thereby evacuated. All of the objects thusevacuated are placed in cars in the youngest train, which was newlycreated during the collection cycle. Certain of the mechanics involvedin the evacuation process are described in more detail in connectionwith similar evacuation performed, as blocks 112 and 114 indicate, inresponse to remembered-set entries.

[0065]FIG. 9 illustrates how the processing represented by block 114proceeds. The entries identify heap regions, and, as block 116indicates, the collector scans the thus-identified heap regions to findreferences to locations in the collection set. As blocks 118 and 120indicate, that entry's processing continues until the collector finds nomore such references. Every time the collector does find such areference, it checks to determine whether, as a result of a previousentry's processing, the referred-to object has already been evacuated.If it has not, the collector evacuates the referred-to object to a(possibly new) car in the train containing the reference, as blocks 122and 124 indicate.

[0066] As FIG. 10 indicates, the evacuation operation includes more thanjust object relocation, which block 126 represents. Once the object hasbeen moved, the collector places a forwarding pointer in thecollection-set location from which it was evacuated, for a purpose thatwill become apparent presently. Block 128 represents that step.(Actually, there are some cases in which the evacuation is only a“logical” evacuation: the car containing the object is simply re-linkedto a different logical place in the collection sequence, but its addressdoes not change. In such cases, forwarding pointers are unnecessary.)Additionally, the reference in response to which the object wasevacuated is updated to point to the evacuated object's new location, asblock 130 indicates. And, as block 132 indicates, any referencecontained in the evacuated object is processed, in an operation thatFIGS. 1A and 1B (“FIG. 11”) depict.

[0067] For each one of the evacuated object's references, the collectorchecks to see whether the location that it refers to is in thecollection set. As blocks 134 and 136 indicate, the reference processingcontinues until all references in the evacuated object have beenprocessed. In the meantime, if a reference refers to a collection-setlocation that contains an object not yet evacuated, the collectorevacuates the referred-to object to the train to which the evacuatedobject containing the reference was evacuated, as blocks 138 and 140indicate.

[0068] If the reference refers to a location in the collection set fromwhich the object has already been evacuated, then the collector uses theforwarding pointer left in that location to update the reference, asblock 142 indicates. Before the processing of FIG. 11, the rememberedset of the referred-to object's car will have an entry that identifiesthe evacuated object's old location as one containing a reference to thereferred-to object. But the evacuation has placed the reference in a newlocation, for which the remembered set of the referred-to object's carmay not have an entry. So, if that new location is not as far forward asthe referred-to object, the collector adds to that remembered set anentry identifying the reference's new region, as blocks 144 and 146indicate. As the drawings indicate, the remembered set may similarlyneed to be updated even if the referred-to object is not in thecollection set.

[0069] Now, some train-algorithm implementations postpone processing ofthe references contained in evacuated collection-set objects until afterall directly reachable collection-set objects have been evacuated. Inthe implementation that FIG. 10 illustrates, though, the processing of agiven evacuated object's references occurs before the next object isevacuated. So FIG. 11's blocks 134 and 148 indicate that the FIG. 11operation is completed when all of the references contained in theevacuated object have been processed. This completes FIG. 10'sobject-evacuation operation, which FIG. 9's block 124 represents.

[0070] As FIG. 9 indicates, each collection-set object referred to by areference in a remembered-set-entry-identified location is thusevacuated if it has not been already. If the object has already beenevacuated from the referred-to location, the reference to that locationis updated to point to the location to which the object has beenevacuated. If the remembered set associated with the car containing theevacuated object's new location does not include an entry for thereference's location, it is updated to do so if the car containing thereference is younger than the car containing the evacuated object. Block150 represents updating the reference and, if necessary, the rememberedset.

[0071] As FIG. 8's blocks 112 and 114 indicate, this processing ofcollection-set remembered sets is performed initially only for entriesthat do not refer to locations in the oldest train. Those that do areprocessed only after all others have been, as blocks 152 and 154indicate.

[0072] When this process has been completed, the collection set's memoryspace can be reclaimed, as block 164 indicates, since no remainingobject is referred to from outside the collection set: any remainingcollection-set object is unreachable. The collector then relinquishescontrol to the mutator.

[0073] FIGS. 12A-12J illustrate results of using the train algorithm.FIG. 12A represents a generation in which objects have been allocated innine car sections. The oldest train has four cars, numbered 1.1 through1.4. Car 1.1 has two objects, A and B. There is a reference to object Bin the root set (which, as was explained above, includes live objects inthe other generations). Object A is referred to by object L, which is inthe third train's sole car section. In the generation's remembered sets170, a reference in object L has therefore been recorded against car1.1.

[0074] Processing always starts with the oldest train's earliest-addedcar, so the garbage collector refers to car 1.1's remembered set andfinds that there is a reference from object L into the car beingprocessed. It accordingly evacuates object A to the train that object Loccupies. The object being evacuated is often placed in one of theselected train's existing cars, but we will assume for present purposesthat there is not enough room. So the garbage collector evacuates objectA into a new car section and updates appropriate data structures toidentify it as the next car in the third train. FIG. 12B depicts theresult: a new car has been added to the third train, and object A isplaced in it.

[0075]FIG. 12B also shows that object B has been evacuated to a new caroutside the first train. This is because object B has an externalreference, which, like the reference to object A, is a reference fromoutside the first train, and one goal of the processing is to formtrains into which there are no further references. Note that, tomaintain a reference to the same object, object L's reference to objectA has had to be rewritten, and so have object B's reference to object Aand the inter-generational pointer to object B. In the illustratedexample, the garbage collector begins a new train for the car into whichobject B is evacuated, but this is not a necessary requirement of thetrain algorithm. That algorithm requires only that externally referencedobjects be evacuated to a newer train.

[0076] Since car 1.1 no longer contains live objects, it can bereclaimed, as FIG. 12B also indicates. Also note that the remembered setfor car 2.1 now includes the address of a reference in object A, whereasit did not before. As was stated before, remembered sets in theillustrated embodiment include only references from cars further back inthe order than the one with which the remembered set is associated. Thereason for this is that any other cars will already be reclaimed by thetime the car associated with that remembered set is processed, so thereis no reason to keep track of references from them.

[0077] The next step is to process the next car, the one whose index is1.2. Conventionally, this would not occur until some collection cycleafter the one during which car 1.1 is collected. For the sake ofsimplicity we will assume that the mutator has not changed anyreferences into the generation in the interim.

[0078]FIG. 12B depicts car 1.2 as containing only a single object,object C, and that car's remembered set contains the address of aninter-car reference from object F. The garbage collector follows thatreference to object C. Since this identifies object C as possiblyreachable, the garbage collector evacuates it from car set 1.2, which isto be reclaimed. Specifically, the garbage collector removes object C toa new car section, section 1.5, which is linked to the train to whichthe referring object F's car belongs. Of course, object F's referenceneeds to be updated to object C's new location. FIG. 12C depicts theevacuation's result.

[0079]FIG. 12C also indicates that car set 1.2 has been reclaimed, andcar 1.3 is next to be processed. The only address in car 1.3'sremembered set is that of a reference in object G. Inspection of thatreference reveals that it refers to object F. Object F may therefore bereachable, so it must be evacuated before car section 1.3 is reclaimed.On the other hand, there are no references to objects D and E, so theyare clearly garbage. FIG. 12D depicts the result of reclaiming car 1.3'sspace after evacuating possibly reachable object F.

[0080] In the state that FIG. 12D depicts, car 1.4 is next to beprocessed, and its remembered set contains the addresses of referencesin objects K and C. Inspection of object K's reference reveals that itrefers to object H, so object H must be evacuated. Inspection of theother remembered-set entry, the reference in object C, reveals that itrefers to object G, so that object is evacuated, too. As FIG. 12Eillustrates, object H must be added to the second train, to which itsreferring object K belongs. In this case there is room enough in car2.2, which its referring object K occupies, so evacuation of object Hdoes not require that object K's reference to object H be added to car2.2's remembered set. Object G is evacuated to a new car in the sametrain, since that train is where referring object C resides. And theaddress of the reference in object G to object C is added to car 1.5'sremembered set.

[0081]FIG. 12E shows that this processing has eliminated all referencesinto the first train, and it is an important part of the train algorithmto test for this condition. That is, even though there are referencesinto both of the train's cars, those cars' contents can be recognized asall garbage because there are no references into the train from outsideof it. So all of the first train's cars are reclaimed.

[0082] The collector accordingly processes car 2.1 during the nextcollection cycle, and that car's remembered set indicates that there aretwo references outside the car that refer to objects within it. Thosereferences are in object K, which is in the same train, and object A,which is not. Inspection of those references reveals that they refer toobjects I and J, which are evacuated.

[0083] The result, depicted in FIG. 12F, is that the remembered sets forthe cars in the second train reveal no inter-car references, and thereare no inter-generational references into it, either. That train's carsections therefore contain only garbage, and their memory space can bereclaimed.

[0084] So car 3.1 is processed next. Its sole object, object L, isreferred to inter-generationally as well as by a reference in the fourthtrain's object M. As FIG. 12G shows, object L is therefore evacuated tothe fourth train. And the address of the reference in object L to objectA is placed in the remembered set associated with car 3.2, in whichobject A resides.

[0085] The next car to be processed is car 3.2, whose remembered setincludes the addresses of references into it from objects B and L.Inspection of the reference from object B reveals that it refers toobject A, which must therefore be evacuated to the fifth train beforecar 3.2 can be reclaimed. Also, we assume that object A cannot fit incar section 5.1, so a new car 5.2 is added to that train, as FIG. 12Hshows, and object A is placed in its car section. All referred-toobjects in the third train having been evacuated, that (single-car)train can be reclaimed in its entirety.

[0086] A further observation needs to be made before we leave FIG. 12G.Car 3.2's remembered set additionally lists a reference in object L, sothe garbage collector inspects that reference and finds that it pointsto the location previously occupied by object A. This brings up afeature of copying-collection techniques such as the typicaltrain-algorithm implementation. When the garbage collector evacuates anobject from a car section, it marks the location as having beenevacuated and leaves the address of the object's new location. So, whenthe garbage collector traces the reference from object L, it finds thatobject A has been removed, and it accordingly copies the new locationinto object L as the new value of its reference to object A.

[0087] In the state that FIG. 12H illustrates, car 4.1 is the next to beprocessed. Inspection of the fourth train's remembered sets reveals nointer-train references into it, but the inter-generational scan(possibly performed with the aid of FIG. 6's card tables) revealsinter-generational references into car 4.2. So the fourth train cannotbe reclaimed yet. The garbage collector accordingly evacuates car 4.1'sreferred-to objects in the normal manner, with the result that FIG. 12Idepicts.

[0088] In that state, the next car to be processed has onlyinter-generational references into it. So, although its referred-toobjects must therefore be evacuated from the train, they cannot beplaced into trains that contain references to them. Conventionally, suchobjects are evacuated to a train at the end of the train sequence. Inthe illustrated implementation, a new train is formed for this purpose,so the result of car 4.2's processing is the state that FIG. 12Jdepicts.

[0089] Processing continues in this same fashion. Of course, subsequentcollection cycles will not in general proceed, as in the illustratedcycles, without any reference changes by the mutator and without anyaddition of further objects. But reflection reveals that the generalapproach just described still applies when such mutations occur.

[0090] A question that arises in connection with incremental collectionis what the rate is at which collection should occur. Too muchcollection can interfere unduly with mutator operation. Too littlecollection can result in the heap's being filled up. The latter resulttypically necessitates a whole-heap collection and, often, anunacceptably long interruption in the mutator's operation. Although thecollection-rate question arises for all incremental collectorsindependently of whether they employ the train algorithm and, to someextent, independently of whether they collect by generations,generational train-algorithm collectors exemplify conventionalapproaches to the problem.

[0091] Consider such a collector that operates, in a “stop-the-world”manner, as was described above in connection with FIGS. 8A and 8B: themutator is stopped until the collector has completed collecting acollection set. In a typical two-generation collector of this type, inwhich most objects are allocated in the young generation initially andthe train algorithm is employed to manage the old generation, into whichthe survivors are later promoted, mutator execution is interrupted for acollector interval whenever the young generation has run out of freespace or soon will. During the ensuing interval, the entire younggeneration is collected, as was explained above. In this sense, theallocation rate is what determines the collection rate.

[0092] As was also explained above, though, at least some collectionintervals include collection not only of the young generation but alsoof a car section in the old generation. So the rate of old-generationcollection must be decided separately. The basic approach described inthe original train-algorithm paper (Hudson and Moss, “IncrementalCollection of Mature Objects,” 1992 Proceedings of the InternationalWorkshop on Memory Management 1992 (Springer-Verlag) is simply to haveone partial old-generation collection for each complete young-generationcollection. That is, every collection interval includes collection ofthe entire young generation and one car section of the old generation.The rate of old-generation collection per unit of allocation istherefore established by the relationship between the car-section andyoung-generation sizes.

[0093] But performance may be improved by having the rate ofold-generation collection vary dynamically in accordance with run-timeconditions. An approach suggested in an M. Sc thesis by Grarup andSeligmann, “Incremental Mature Garbage Collection,” is therefore not tohave a partial old-generation collection occur during every collectioninterval but rather to have one occur only during each N^(th) collectioninterval, where N is a function of how much garbage recentold-generation collections found.

[0094] Another approach, the one used in the HotSpot Java VirtualMachine, is to vary N in accordance with how many bytes of objectpromotion have occurred during young-generation collections and to varythe size of the young generation in accordance with that amount, too.Specifically, a quantity M, which has a range of −2 to 4, is decrementedduring each collection interval in which objects that exceed somethreshold number of bytes have been promoted. M is incremented in otherintervals. If M>1, then a collection interval including old-generationcollection occurs once every M collection intervals. If M≦1,old-generation collection occurs every collection interval. The size ofthe young generation is reduced to 1/(2−M) of its normal size if M<1. Inan application in which more objects tend to be promoted into the oldgeneration, therefore, the rate of old-generation collection isrelatively high, whereas applications that have higher “infantmortality” and thus tend to promote objects into the old generation at alower rate will have a rate of old-generation collection that is lower.

SUMMARY OF THE INVENTION

[0095] But I have recognized that collector performance can be improvedif the incrementally collected generation's rate of collection iscontrolled by varying the collection-set size in accordance with therate of allocation in that same generation. Preferably, the functionused in arriving at the collection-set size involves taking an averageof the amounts of allocation in that generation that have occurredbetween successive incremental collections. For example, a weightedcombination of separate running averages for objects of sizes 0-64 kB,64 kB-256 kB, 256 kB-1 MB, and over 1 MB may be taken over the last 32,64, 128, and 256 collections, respectively.

[0096] The allocation-rate-based function used to determinecollection-set size may additionally include other factors, such as therate at which objects survive incremental collection. Intrain-algorithm-based collectors, the calculation may also take intoaccount how far back in the train sequence, on average, are the cars towhich evacuated objects are moved, and how large the old-generationcars' remembered sets tend to be.

BRIEF DESCRIPTION OF THE DRAWINGS

[0097] The invention description below refers to the accompanyingdrawings, of which:

[0098]FIG. 1, discussed above, is a block diagram of a computer systemin which the present invention's teachings can be practiced;

[0099]FIG. 2 is, discussed above, is a block diagram that illustrates acompiler's basic functions;

[0100]FIG. 3, discussed above, is a block diagram that illustrates amore-complicated compiler/interpreter organization;

[0101]FIG. 4, discussed above, is a diagram that illustrates a basicgarbage-collection mechanism;

[0102]FIG. 5, discussed above, is a similar diagram illustrating thatgarbage-collection approach's relocation operation;

[0103]FIG. 6, discussed above, is a diagram that illustrates agarbage-collected heap's organization into generations;

[0104]FIG. 7, discussed above, is a diagram that illustrates ageneration organization employed for the train algorithm;

[0105]FIGS. 8A and 8B, discussed above, together constitute a flow chartthat illustrates a garbage-collection interval that includesold-generation collection;

[0106]FIG. 9, discussed above, is a flow chart that illustrates in moredetail the remembered-set processing included in FIG. 8A;

[0107]FIG. 10, discussed above, is a block diagram that illustrates inmore detail the referred-to-object evacuation that FIG. 9 includes;

[0108]FIGS. 11A and 11B, discussed above, together form a flow chartthat illustrates in more detail the FIG. 10 flow chart's step ofprocessing evacuated objects' references;

[0109] FIGS. 12A-12J, discussed above, are diagrams that illustrate acollection scenario that can result from using the train algorithm;

[0110]FIGS. 13A and 13B together constituted a flow chart thatillustrates a collection interval, as FIGS. 8A and 8B do, butillustrates optimizations that FIGS. 8A and 8B do not include;

[0111]FIG. 14 is a simplified block diagram that illustrates operationsinvolved in allocating space in the old generation;

[0112]FIG. 15 is a diagram that illustrates a counter array used indetermining the average rate of allocation in the old generation; and

[0113]FIG. 16 is a similar diagram depicting a plurality of such arrays.

DETAILED DESCRIPTION OF AN ILLUSTRATIVE EMBODIMENT

[0114] It will become apparent that the application's broader aspectsare applicable to any incremental collector. However, I prefer to use itin a collector that treats the collected heap as divided into twogenerations and employs the train algorithm for the older generation.The way in which I use the train algorithm is similar to theconventional approach described above, but it includes someoptimizations, as FIGS. 13A and 13B (together, “FIG. 13”) show. And,whereas it was tacitly assumed in the train-algorithm discussion abovethat, as is conventional, only a single car section would be collectedin any given collection increment, FIG. 13 reflects the possibility ofmultiple-car collection sets.

[0115] Blocks 172, 176, and 178 represent operations that correspond tothose that FIG. 8's blocks 102, 106, and 108 do, and dashed line 174represents the passage of control from the mutator to the collector, asFIG. 8's dashed line 104 does. For the sake of efficiency, though, thecollection operation of FIG. 13 includes a step represented by block180. In this step, the collector needs to determine the collection-setsize, but we will postpone the discussion of that determination untilafter the overview of FIG. 13's incremental collection is finished. Withthat determination made, the collector reads the remembered set of eachcar in the collection set to determine the location of each referenceinto the collection set from a car outside of it, it places the addressof each reference thereby found into a scratch-pad list associated withthe train that contains that reference, and it places the scratch-padlists in reverse-train order. Block 180 represents all those operations.As blocks 182 and 184 indicate, the collector then processes the entriesin all scratch-pad lists but the one associated with the oldest train.

[0116] Before the collector processes references in that train'sscratch-pad list, the collector evacuates any objects referred to fromoutside the old generation, as block 186 indicates. To identify suchobjects, the collector scans the root set. In some generationalcollectors, it may also have to scan other generations for referencesinto the collection set. For the sake of example, though, we haveassumed the particularly common scheme in which a generation'scollection in a given interval is always preceded by complete collectionof every (in this case, only one) younger generation in the sameinterval. If, in addition, the collector's promotion policy is topromote all surviving younger-generation objects into older generations,it is necessary only to scan older generations, of which there are nonein the example; i.e., some embodiments may not require that any othergeneration be scanned in the block-186 operation.

[0117] For those that do, though, the scanning may actually involveinspecting each surviving object in the young generation, or thecollector may expedite the process by using card-table entries.Regardless of which approach it uses, the collector immediatelyevacuates into another train any collection-set object to which itthereby finds an external reference. The typical policy is to place theevacuated object into the youngest such train. As before, the collectordoes not attempt to evacuate an object that has already been evacuated,and, when it does evacuate an object to a train, it evacuates to thesame train each collection-set object to which a reference in thethus-evacuated object refers. In any case, the collector updates thereference to the evacuated object.

[0118] When the inter-generational references into the generation havethus been processed, the garbage collector determines whether there areany references into the oldest train from outside that train. If not,the entire train can be reclaimed, as blocks 188 and 190 indicate.

[0119] As block 192 indicates, the collector interval typically endswhen a train has thus been collected. If the oldest train cannot becollected in this manner, though, the collector proceeds to evacuate anycollection-set objects referred to by references whose locations theoldest train's scratch-pad list includes, as blocks 194 and 196indicate. It removes them to younger cars in the oldest train, againupdating references, avoiding duplicate evacuations, and evacuating anycollection-set objects to which the evacuated objects refer. When thisprocess has been completed, the collection set can be reclaimed, asblock 198 indicates, since no remaining object is referred to fromoutside the collection set: any remaining collection-set object isunreachable. The collector then relinquishes control to the mutator.

[0120] Having reviewed the sequence of operations employed to collect asingle collection set, we now turn to the manner in which the minimumcollection-set size for any given incremental collection is determined.The number of cars that will be determined is only a minimum. As wasnoted above, a whole train's worth of cars may be reclaimed, even in acollection increment in which reachable objects have been culled fromonly a much smaller number of cars. Also, as is described in my commonlyassigned co-pending U.S. patent application for Detection of DeadRegions during Incremental Collection, which was filed on the same dateas the present application and is hereby incorporated by reference,additional cars may be added to the collection set if they can berecognized as containing only garbage without being culled for reachableobjects. But those additions depend on the particular set of referencesencountered when the collection increment occurs. The method to bedescribed, on the other hand, determines a collection increment'sminimum amount of collection.

[0121] As was explained above, the collection-set size is based on therate of allocation in the incrementally collected generation, in thiscase, the old generation. Preferably, the allocations upon which thecollection-set-size determinations are made include not only allocationsresulting from promotions from the young generation but also any directallocations that may occur. Although two-generational collectorstypically perform most initial object allocation in the younggeneration, many also bypass the young generation under specialcircumstances and allocate objects directly into the old generation.This may happen, for instance, if the object is especially large. Theyoung generation is typically much smaller than the old generation, andan extremely large object may not even fit in it. Even if it does fit,the possibility of thereafter having to copy a very large object intothe old generation if it meets promotion criterion may cause thecollector to allocate such objects directly into the old generation tobegin with. The illustrated embodiment monitors such direct allocationsin addition to the typically more-common promotion-triggeredallocations.

[0122] The particular way in which a collector monitors such allocationsis not critical to obtaining the present invention's advantages. ButFIG. 14 is a flow chart that depicts in simplified form an exampleallocation routine that includes steps for allocation-rate monitoring.Block 200 represents determining which generation to allocate the objectinto. Actually, that block is somewhat conceptual; typically there are anumber of allocation routines, and the decision represented by block 200will often be made implicitly by the selection of routine. Some routinesare the ones that the mutator calls, and others are called by thegarbage collector in the process of responding to a promotion decision.In the latter case, the decision has implicitly been made already toallocate the object into the old generation. In the former case,although the routine that the mutator calls may indeed include making adecision such as the one that block 200 represents, it may instead be aroutine to which the mutator is directed by the object's classstructures, in which case the determination of whether to allocate theobject into the young generation or into the old generation is againmade implicitly, this time by selecting which class to instantiate.Independently of whether new-car allocation was necessary, space for thenew object is allocated in the current car, as block 21 indicates, andthe allocation routine returns.

[0123] In any event, most initial object allocations do not involve theold generation. As block 202 indicates, such allocations simply resultin the object's being allocated in the young generation. When an objectis to be allocated in the old generation, on the other hand—whetherbecause it is being promoted or because it is being allocateddirectly—the object needs to be placed into a car. The car is usuallyone that has already been allocated and contains at least one otherobject. If the last-allocated car runs out of space, though, a new caris allocated, as blocks 204 and 206 indicate. If this happens, theroutine increments a counter that represents the number of bytesallocated-since the last collection increment, as block 208 indicates.That is, it adds to that counter the number of bytes that thejust-completed car contains. That number can be inferred, for example,from that car's free pointer. Independently of whether new-carallocation was necessary, space for the new object is allocated in thecurrent car, as block 210 indicates, and the allocation routine returns.

[0124] As was mentioned above, the particular way in which the collectormonitors allocation rate is not critical. For example, monitoring stepsneed not be included in every allocation operation, as they are in theFIG. 14 allocation routine. While it may be considered desirable to usethe approach of FIG. 14 to keep track of direct allocation into the oldgeneration, for instance, keeping track of allocations due to promotionsmay be better implemented by simply comparing the cars' collectiveoccupancy before a young-generation collection with their occupancyafter it.

[0125] When the next old-generation collection increment arrives, thecounter's value tells how much allocation has occurred in the oldgeneration since the previous increment. There may be some embodimentsof the present invention that base a collection set's size on thatamount alone. I prefer not to adopt such an approach, though, because ittends to make the collection-set size “bursty.” Instead, I average theamount of allocation over a number of previousinter-collection-increment intervals.

[0126] As FIG. 15 illustrates, one can employ a counter array 212 forthis purpose. Each entry in that array contains a count representing thenumber of bytes allocated during a different inter-collection-incrementinterval. A counter pointer 214 identifies the array slot that containsthe current interval's count. The value in that slot is the one that isincremented in the step that FIG. 14's block 208 represents. At the endof each old-generation collection increment, the counter pointer's valueis changed to point to the next slot in the array. This occurscircularly: if the current slot is the bottom slot, then the pointer ischanged to point to the top slot. Additionally, the contents of thenewly pointed-to slot are cleared to indicate that no new cars have yetbeen allocated during the inter-collection-increment interval that hasjust started. The value that prevails in that slot at the beginning ofthe next old-generation collection increment is averaged with the valuesin all of the other slots to produce the allocation-rate value used todetermine that increment's collection-set size. That average may be astraight average or, say, an average weighted to emphasize more-recentvalues.

[0127] As was explained above, the averaging is performed to avoid thewide variations in collection-set size in which typically bursty objectallocation could result. The greater the size of the array, the more theallocation rate's bursty nature will be suppressed. Suppressing thatbursty nature adequately in the face of a mutator that calls forallocation of truly enormous objects at widely isolated times, though,may require an array size so great as to detract undesirably from thecollection-set size's responsiveness to allocation rate. To achieve acompromise between smoothing and responsiveness, one can adopt anapproach that FIG. 16 illustrates.

[0128] That approach employs a plurality of different-sized arrays 216a-d together with respective pointers 218 a-d. The smallest array 216 amay have only, say, thirty-two slots, and the slot that its associatedpointer identifies is the one that is incremented whenever allocation ofan object requires allocation of only a regular-sized car. Some objectsare too large for such a car, though. Such objects require oversizedcars. The current counter in array 216 a is incremented (by a largernumber of bytes) for such cars, too, if they are no larger than, say, 64kilobytes. If the number of cars allocated at one time requires between64 kilobytes and 256 kilobytes, on the other hand, a counter in array216 b is incremented. A counter in array 216 c is incremented forallocations between 256 kilobytes and 1 megabyte, and allocations inexcess of 1 megabyte cause a counter in an array 216 d to beincremented. By taking a weighted average of the different arrays'totals, the collector can remain responsive to changes in the rate atwhich most allocations occur but appropriately smooth the contributionsfrom isolated large-object allocations.

[0129] Of course, other averaging techniques could be employed insteadto arrive at an allocation-rate value. One could dispense with the useof arrays entirely by using exponential averages, for example, and suchan approach may work reasonably well. I prefer the array approach,though, because it appears to provide an appropriate degree ofresponsiveness without resulting in an undue sensitivity to themost-recent value.

[0130] Once a value has been determined for the average amount ofold-generation allocation that occurs between old-generation collectionincrements, the collector calculates a desired initial collection-setsize from it. The most-straightforward way of arriving at an initialcollection-set size is merely to multiply the average allocation rate bysome coefficient:

G=CB  (1)

[0131] where G is the goal, in bytes, for the collection-set cars' totalobject space; C is a proportionality coefficient, and B is the averagenumber of bytes allocated in the old generation betweenold-generation-collection increments. With the goal thus determined,cars are added to the collection set until the total number of objectbytes in each car equals or exceeds the goal. The total number of objectbytes is readily determined from each car's free pointer, which tellswhere the next object, if any, should be added to that car. The carsthus selected are culled of reachable objects, if necessary, to enabletheir space to be reclaimed. (Again, cars that can be reclaimed withoutculling may be added to the initial collection whose number thiscalculation determines.)

[0132] Although the amount reclaimed in a collection increment can begreater than the initial collection-set size, this happens onlyinfrequently, so the value of C should be greater than unity. Suppose,for example, that a fraction s′ of the bytes in a collection set areexpected to survive on the average. That means that the average amountreclaimed will be (1−s′) G. The goal is that the amount reclaimedshould, on a steady-state basis, equal the amount allocated, so theproportionality coefficient C is related to the survival rate in thefollowing manner. $\begin{matrix}{C = \frac{1}{1 - s^{\prime}}} & (2)\end{matrix}$

[0133] Of course, the survival rate varies from application toapplication and from time to time within the same application, so manyembodiments will monitor the survival rate and adapt the proportionalitycoefficient to it. Survival rate is readily determined from the factthat any objects added to cars during an old-generation collectionincrement are objects that survived that collection increment.

[0134] Suppose, for example, that at the beginning of a collection agiven train contains four cars and that the value of the fourth car'sfree pointer at that time shows that objects occupy 800 bytes in thatcar. Suppose further that, at the end of the collection increment, thattrain has six cars and that the fourth, fifth, and sixth cars' freepointers show that objects occupy 900, 700, and 200 bytes, respectively,at that time. Then that train has received 900−800+700+200=1000 bytes ofsurviving objects. Similar calculations for all other trains yield thetotal survival amount, and the ratio of this value to the total numberof bytes that collection-set objects occupied at the beginning of thecollection increment gives the survival rate. Preferably, the rate for agiven increment is averaged in some fashion with the rates for previousincrements to arrive at a recent survival rate s.

[0135] This value could be substituted in equation (2) to produce theproportionality coefficient. However, because that calculation involvesa subtraction in the numerator and s may approach unity in someincrements, the resultant collection-set size could end up beingimpracticably large. Of course, averaging the survival rates over alarge number of old-generation collection increments will tend to reducethat danger, but taking the average over too many increments may preventthe collection-set-size calculation from being responsive enough to thesurvival rate.

[0136] I therefore prefer a calculation approach that retains somemeasure of responsiveness but eliminates the possibility of excessivelyhigh proportionality values. This approach is based on recognizing that,if the monitored survival rate s replaces the expected survival rate s′in equation (2), the Taylor-series expansion of that equation becomes$\begin{matrix}{C = {1 + {\sum\limits_{k = 1}^{\infty}\quad {s\quad}^{k}}}} & (3)\end{matrix}$

[0137] By truncating that series and substituting the result intoequation (1), we obtain

G=(1+s)B  (4)

[0138] That calculation is not susceptible to the exceedingly largevalues that can result from subtraction in a numerator. Since it doesresult from truncating the Taylor series, though, it will tend toundershoot the desired collection-set size, so I employ a constant K tomake the undershoot less likely:

G=KB(1+s)  (5)

[0139] K's value is chosen to reverse the expected undershoot. That is,if s′ is the expected survival rate, then the value for K that resultsfrom taking the reciprocal of the undershoot ratio is given by:$\begin{matrix}{K = {\frac{\frac{1}{1 - s^{\prime}}}{1 + s^{\prime}} = \frac{1}{1 - s^{\prime \quad 2}}}} & (6)\end{matrix}$

[0140] The expected survival rate is treated as a fixed quantity in theabove discussion, and embodiments that compute their targetcollection-set sizes in accordance with those equations may indeedemploy a fixed value; a value of about 0.6, for example, seems to workwell for a wide variety of applications. But other embodiments mayupdate s′ by averaging over a longer period of time the same data theyuse to update s. To avoid storing excessively long records for thispurpose, embodiments that take this approach may use an exponentialaverage.

[0141] The collection-set size can additionally be made dependent onother run-time quantities. In the case of a collector that employs thetrain algorithm, for instance, one may attempt to take into account somemeasure of how effectively the collector is currently grouping garbagestructures together. A metric that can be helpful in this context is the“distance” by which evacuated objects are moved. As was explained above,the train ordering and the ordering of cars within trains impose anoverall order on the generation's cars. When an object is evacuated, itmay be evacuated all the way back to the end of that sequence: it may bemoved a relatively large distance. If it ends up near the front of thesequence, on the other hand, the distance it has moved is relativelysmall.

[0142] One way to arrive at a normalized distance value involvesnumbering the cars in accordance with their sequence positions, the carfarthest to the rear being given the highest number. The number of bytesof objects evacuated to a given car during the collection increment ismultiplied by that car's sequence number, and the results for all carsare added together and divided by the product of the highest sequencenumber and the total number of evacuated-object bytes. An alternative tousing car-sequence numbers as multipliers is to use the number of bytesof object storage contained by cars that precede the locations to whichobjects are evacuated. Although this approach adds more complexity, italso yields greater accuracy, since not all cars contain the same objectvolume. The resultant value δ is an indicator of how far on averageevacuated objects have been moved. As is the case with the otherquantities, the distance value used in the calculation may actually bethe result of averaging distance values over a number of previousincrements.

[0143] Observation of train-algorithm operation reveals that shortevacuation distances tend to be symptomatic of a local topology thatrequires a relatively large amount of work to group a data structureproperly. An example occurs when the oldest train includes a lot ofreferences from its younger cars to its older cars but the onlyreferences into the train from outside of it are (initially) located inits youngest car. In such a situation, it takes a lot of short-distanceevacuations to collect the cars at the beginning of the train, and it isonly after collection reaches what had been the end of the train thateither the train becomes garbage or the objects that it contains getremoved to younger trains. In such a situation, it is desirable toincrease the collection rate so as to pass quickly through the partwhere there is a lot of “straightening,” i.e., a lot of placingreferred-to objects behind the references to them. A shorter distancevalue should therefore result in a greater goal value, so one may employa function such as the following:

G=KB(1+s)(2−δ)  (7)

[0144] Although I have employed that function, I have found thatdistance effects are most pronounced at short distances, so squaring 1−δyields an improvement. Instead of substituting (1−δ)² for 1−δ inequation (7), though, I have used the following function:

G=KB[1+s+s(1−δ)²],  (8)

[0145] effectively making the distance factor's contribution depend onthe object volume that moved the measured distance.

[0146] The tests that I have performed by using that function haveyielded collection-set sizes that desirably cause the rate ofreclamation to converge to the rate of allocation and thus enable thecollector to operate for extensive periods in the incremental modewithout resorting to full collections. But embodiments of the presentinvention may add further refinements. For example, one may want toincrease the collection rate if the proportion of remembered setsexceeding some threshold size is relatively large, so the function canadditionally include some metric reflecting that factor. A factorreflecting the amount of free space in the heap may also be included;the goal collection-set size for a given allocation rate may be made toincrease if the amount of free space has fallen to a relatively lowlevel. In short, there are many ways that can be used to basecollection-set size on the rate of allocation in the generation beingincrementally collected.

[0147] Much of this approach's advantages in the context of atrain-algorithm-based collector results from its implementingmultiple-car collection sets. To appreciate some of these advantages, itis important to recognize that, although this technique itself is usedto arrive at a collection-set size, it may actually be used in practiceas a way to determine other parameters after a desired collection-setsize has been determined.

[0148] For example, it may be considered desirable to employ the maximumcollection-set size that is consistent with some predeterminedpause-time limits. If the collection-set size determined in accordancewith the above-described technique from the old-generation allocationrate is significantly below the maximum collection-set size that allowedpause times will permit, the collector may increase the amount of timebetween the old (or other incrementally collected) generation'scollection increments.

[0149] Thus increasing the time between collection increments improvescollection efficiency, because it gives objects more time to die. Italso yields economies of scale, since it enables the collector toamortize over a relatively large amount of collection those costs of acollection increment that are relatively independent of how muchcollection that increment performs. An example of such a cost is that ofrequiring the operating system to suspend all of the program's mutatorthreads. These costs can be significant, but they are essentially thesame for, say, a ten-car collection as they are for a single-carcollection. Similarly, the cost of scanning all roots for referencesinto the collection set is not significantly greater for a largecollection set then for a small one. So garbage collection's overallcost can be reduced significantly by making collection sets as large aspause-time limits permit.

[0150] The multiple-car aspect of the collection set can additionallyyield memory savings. Suppose, for example, that every collection setconsisted of only a single car and that cars were therefore so sized asto result in a collection-increment duration that is just below themaximum tolerable duration. Since car sections are the increments bywhich trains grow, employing the resultant large car-section size couldtend to waste space; as the number of trains becomes large, the spacewasted in partially filled cars can become significant.

[0151] Using multiple-car collection sets can also reduce evacuationcost. As was mentioned above, a collection-set object that a referenceoutside the collection set refers to must be evacuated into the train towhich the object containing that reference belongs. But the evacuationcan be avoided if the car containing the reference is in the collectionset, too. Having more cars in the collection set therefore tends toreduce the number of evacuations and thus the overall collection cost.

[0152] Finally, employing multiple-car collection sets facilitates useof a popular-object-handling approach described in my U.S. Pat. No.6,434,576 for Popular-Object Handling in a Train-Algorithm-Based GarbageCollector. That application describes a convenient run-time mechanismfor assigning objects to their own, single-object cars and for makingthose cars' sizes significantly smaller than normal-car sizes. This isbeneficial for “popular” objects, i.e., for objects to which manyreferences refer. The need to update all of the references to themordinarily makes evacuating popular objects onerous. But that cost canbe avoided if each popular object is in a respective (typically small)single-object car. In that case, the car's membership can be changed bysimply re-linking the car, so no reference updating is needed. By usingmultiple-car collection sets, one can perform a relatively large amountof collection in an interval even though individual cars are small.

[0153] But the present invention's advantages extend beyond the factthat it supports multiple-car collection sets when it is employed in thetrain algorithm. Principal among these is the accuracy that results frombasing the collection-set size on the total rate of old-generationallocation.

[0154] To appreciate this, consider the previously described prior-artapproach of basing the frequency of old-generation collection on howproductive an old-generation collection is at reclaiminggarbage-containing memory space. The rationale for that approach is thatthe overall frequency of (mostly young-generation) collections isalready based on the rate at which objects are allocated, and collectionefficiency is, according to the rationale, beneficially affected byreducing the ratio of old-generation collections to young-generationcollections if the garbage ratio has fallen. That is, if the garbageratio is too low, the collector is not waiting long enough betweenold-generation-collection intervals, so objects have not had enough timeto “die.”

[0155] Although this rationale has a surface appeal, I have recognizedthat applying it can actually be counterproductive. In practice, thetrain algorithm tends to “clump” long-lived objects into the sametrains. As the old-generation collection encounters such sequences oflong-lived objects, it observes a low rate of garbage recovery. Inaccordance with that prior-art technique's rationale, the frequency ofold-generation-collection intervals is too high to allow enough objectstime to die. In fact, though, the frequency should be higher so that thecollector moves more quickly through the long-lived objects to reachones that are likely to be garbage and whose collection can thus free upthe memory needed for further allocation. So thus basing theold-generation-collection rate only on garbage ratio can actually tendto impede necessary memory-space reclamation.

[0156] Now, not all prior workers rejected the concept of basing theold-generation-collection rate on allocation into the old generation. Aswas explained above, the HotSpot collector varied both theyoung-generation size and the number of young-generation collectionintervals between old-generation-collection intervals in accordance withpromotions into the old generation. But that did not afford theadvantages that result from using collection-set-size changes as the wayto varying collection rate. By choosing old-generation-collectionfrequency rather than collection-set size as the way in which to varycollection rate, that prior-art approach can cause a vicious cycle,because a relatively large amount of promotion during a young-generationcollection tends to increase the frequency with whichold-generation-collection cycles occur. This gives the younggeneration's occupants less time to die and therefore causes an increasein promotion rate, which in turn causes a further increase in thefrequency of old-generation collection cycles. As a consequence,collection efficiency decreases: less memory space is collected for agiven amount of collection. And this effect tends to feed upon itself.The present invention's approach of instead varying collection-set sizeprovides a way of breaking this vicious cycle; the collection rate canbe increased without increasing frequency and without thereby affectingthe collection operation's efficiency adversely.

[0157] Moreover, a feature of the illustrated embodiment greatly reducesthe likelihood that the incremental collection will fail to keep up withthe allocation and thereby trigger a disruptive complete-generationcollection. Specifically, the illustrated embodiment bases itsdetermination of the target collection rate on the total old-generationallocation rate, i.e., on the total not only of promotion but also ofdirect allocation into the old generation, whereas the HotSpotcollector, for example, bases its collection rate only on the portion ofthe old-generation allocation that results from promotion, while theallocation on whose rates other previous collectors mentioned above basetheir rate of old-generation collection does not occur in the samegeneration at all. Since it is often the very largest objects that arethe subjects of direct allocation, it is not at all difficult to imaginean application in which basing collection rate on promotion only—or,worse, on no allocation in the same generation at all will fail torespond to most old-generation allocation. By basing collection-set sizeon the generation's total allocation rate, on the other hand, theillustrated embodiment is more likely to avoid the resultant need fordisruptive full-generation collection cycles.

[0158] The present invention therefore constitutes a significant advancein the art.

What is claimed is:
 1. For employing a computer system, which includesmemory of which at least some is used as a heap for dynamic allocation,to perform garbage collection incrementally on an incrementallycollected generation of the heap, a method comprising: A) monitoring therate of allocation within the incrementally collected generation; B)calculating collection-set sizes from the allocation rates thusmonitored; and C) collecting the generation in increments that employcollection sets whose minimum sizes are the collection-set sizes thusdetermined.
 2. A method as defined in claim 1 further comprising: A)treating the heap as additionally including at least one youngergeneration, into which objects are allocated; B) promoting into theincrementally collected generation from the at least one youngergeneration objects that meet predetermined promotion criteria.
 3. Amethod as defined in claim 2 wherein: A) the method further comprisesallocating objects directly into the incrementally collected heap; andB) the monitored allocation rate includes not only the rate at whichobjects are promoted into the incrementally collected heap but also therate at which they are allocated into it directly.
 4. A method asdefined in claim 1 wherein the calculation of collection-set sizesincludes determining the allocation rate from the volumes of allocationin the incrementally collected generation that occur during intervalsbetween increments of the incrementally collected generation'scollection.
 5. A method as defined in claim 4 wherein: A) the methodfurther includes monitoring the rate at which objects in the generationsurvive collection; and B) the calculation of collection-set sizesincludes calculating the collection-set sizes from the rate thusmonitored as well as from the allocation rate.
 6. A method as defined inclaim 4 wherein: A) the method further includes monitoring the distancesthrough which objects are evacuated during collection; and B) thecalculation of collection-set sizes includes calculating thecollection-set sizes from the distances thus monitored as well as fromthe allocation rate.
 7. A method as defined in claim 4 wherein thecalculation of a collection-set size includes determining the allocationrate from the volumes of allocation in the incrementally collectedgeneration that occur during a plurality of previous intervals betweenincrements of the incrementally collected generation's collection.
 8. Amethod as defined in claim 7 further including: A) monitoring the rateat which objects in the generation survive collection; and B) thecalculation of collection-set sizes includes calculating thecollection-set sizes from the rate thus monitored as well as from theallocation rate.
 9. A computer system comprising: A) processor circuitryoperable to execute processor instructions; and B) memory circuitry, towhich the processor circuitry is responsive, that contains processorinstructions readable by the processor circuitry to configure it totreat at least a heap portion of the memory as a heap, in which dynamicallocation occurs, and act as a garbage collector that incrementallycollects an incrementally collected generation of the heap by: i)monitoring the rate of allocation within the incrementally collectedgeneration; ii) calculating collection-set sizes from the allocationrates thus monitored; and iii) collecting the generation in incrementsthat employ collection sets whose minimum sizes are the collection-setsizes thus determined.
 10. A computer system as defined in claim 9wherein the garbage collector: A) treats the heap as additionallyincluding at least one younger generation, into which objects areallocated; B) promotes into the incrementally collected generation fromthe at least one younger generation objects that meet predeterminedpromotion criteria.
 11. A computer system as defined in claim 10wherein: A) the garbage collector allocates objects directly into theincrementally collected heap; and B) the monitored allocation rateincludes not only the rate at which objects are promoted into theincrementally collected heap but also the rate at which they areallocated into it directly.
 12. A computer system as defined in claim 9wherein the calculation of collection-set sizes includes determining theallocation rate from the volumes of allocation in the incrementallycollected generation that occur during intervals between increments ofthe incrementally collected generation's collection.
 13. A computersystem as defined in claim 12 wherein: A) the garbage collector monitorsthe rate at which objects in the generation survive collection; and B)the calculation of collection-set sizes includes calculating thecollection-set sizes from the rate thus monitored as well as from theallocation rate.
 14. A computer system as defined in claim 12 wherein:A) the garbage collector monitors the distances through which objectsare evacuated during collection; and B) the calculation ofcollection-set sizes includes calculating the collection-set sizes fromthe distances thus monitored as well as from the allocation rate.
 15. Acomputer system as defined in claim 12 wherein the calculation of acollection-set size includes determining the allocation rate from thevolumes of allocation in the incrementally collected generation thatoccur during a plurality of previous intervals between increments of theincrementally collected generation's collection.
 16. A computer systemas defined in claim 15 wherein: A) the garbage collector monitors therate at which objects in the generation survive collection; and thecalculation of collection-set sizes includes calculating thecollection-set sizes from the rate thus monitored as well as from theallocation rate.
 17. A storage medium containing instructions readableby a computer system that includes memory to configure the computersystem to treat at least a portion of the memory as a heap, in whichdynamic allocation occurs, and act as a garbage collector thatincrementally collects an incrementally collected generation of the heapby: A) monitoring the rate of allocation within the incrementallycollected generation; B) calculating collection-set sizes from theallocation rates thus monitored; and C) collecting the generation inincrements that employ collection sets whose minimum sizes are thecollection-set sizes thus determined.
 18. A storage medium as defined inclaim 17 wherein the garbage collector: A) treats the heap asadditionally including at least one younger generation, into whichobjects are allocated; B) promotes into the incrementally collectedgeneration from the at least one younger generation objects that meetpredetermined promotion criteria.
 19. A storage medium as defined inclaim 18 wherein: A) the garbage collector allocates objects directlyinto the incrementally collected heap; and B) the monitored allocationrate includes not only the rate at which objects are promoted into theincrementally collected heap but also the rate at which they areallocated into it directly.
 20. A storage medium as defined in claim 17wherein the calculation of collection-set sizes includes determining theallocation rate from the volumes of allocation in the incrementallycollected generation that occur during intervals between increments ofthe incrementally collected generation's collection.
 21. A storagemedium as defined in claim 20 wherein: A) the garbage collector monitorsthe rate at which objects in the generation survive collection; and B)the calculation of collection-set sizes includes calculating thecollection-set sizes from the rate thus monitored as well as from theallocation rate.
 22. A computer system as defined in claim 20 wherein:A) the garbage collector monitors the distances through which objectsare evacuated during collection; and B) the calculation ofcollection-set sizes includes calculating the collection-set sizes fromthe distances thus monitored as well as from the allocation rate.
 23. Astorage medium as defined in claim 20 wherein the calculation of acollection-set size includes determining the allocation rate from thevolumes of allocation in the incrementally collected generation thatoccur during a plurality of previous intervals between increments of theincrementally collected generation's collection.
 24. A storage medium asdefined in claim 23 wherein: A) the garbage collector monitors the rateat which objects in the generation survive collection; and B) thecalculation of collection-set sizes includes calculating thecollection-set sizes from the rate thus monitored as well as from theallocation rate.
 25. An electromagnetic signal representing sequences ofinstructions that, when executed by a computer system that includesmemory, configure the computer system to treat at least a portion of thememory as a heap, in which dynamic allocation occurs, and act as agarbage collector that incrementally collects an incrementally collectedgeneration of the heap by: A) monitoring the rate of allocation withinthe incrementally collected generation; B) calculating collection-setsizes from the allocation rates thus monitored; and C) collecting thegeneration in increments that employ collection sets whose minimum sizesare the collection-set sizes thus determined.
 26. An electromagneticsignal as defined in claim 25 wherein the garbage collector: A) treatsthe heap as additionally including at least one younger generation, intowhich objects are allocated; B) promotes into the incrementallycollected generation from the at least one younger generation objectsthat meet predetermined promotion criteria.
 27. An electromagneticsignal as defined in claim 26 wherein: A) the garbage collectorallocates objects directly into the incrementally collected heap; and B)the monitored allocation rate includes not only the rate at whichobjects are promoted into the incrementally collected heap but also therate at which they are allocated into it directly.
 28. Anelectromagnetic signal as defined in claim 25 wherein the calculation ofcollection-set sizes includes determining the allocation rate from thevolumes of allocation in the incrementally collected generation thatoccur during intervals between increments of the incrementally collectedgeneration's collection.
 29. An electromagnetic signal as defined inclaim 28 wherein: A) the garbage collector monitors the rate at whichobjects in the generation survive collection; and B) the calculation ofcollection-set sizes includes calculating the collection-set sizes fromthe rate thus monitored as well as from the allocation rate.
 30. Acomputer system as defined in claim 28 wherein: A) the garbage collectormonitors the distances through which objects are evacuated duringcollection; and B) the calculation of collection-set sizes includescalculating the collection-set sizes from the distances thus monitoredas well as from the allocation rate.
 31. An electromagnetic signal asdefined in claim 28 wherein the calculation of a collection-set sizeincludes determining the allocation rate from the volumes of allocationin the incrementally collected generation that occur during a pluralityof previous intervals between increments of the incrementally collectedgeneration's collection.
 32. An electromagnetic signal as defined inclaim 31 wherein: A) the garbage collector monitors the rate at whichobjects in the generation survive collection; and B) the calculation ofcollection-set sizes includes calculating the collection-set sizes fromthe rate thus monitored as well as from the allocation rate.
 33. Forincrementally collecting an incrementally collected generation of a heapwhere dynamic allocation occurs in a computer system's memory, a garbagecollector including: A) means for monitoring the rate of allocationwithin the incrementally collected generation; B) means for calculatingcollection-set sizes from the allocation rates thus monitored; and C)means for collecting the generation in increments that employ collectionsets whose minimum sizes are the collection-set sizes thus determined.